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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is live

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Speaker 1: from coast to coast with Caroline Hide in New York

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Speaker 1: and Ed Lovelow in sentances.

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Speaker 2: Go this is Bloomberg Tech coming up us Paul's TSMC's

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Speaker 2: waiver on essentral gear to its China chip making facility,

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Speaker 2: adding further curbs to its semiconduction production capabilities, plus.

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Speaker 3: Tech stock slide to start September im in a global

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Speaker 3: sell off with all Magnificent seven names in the red.

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Speaker 3: We dig into the fourth day of losses for Nvidio,

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Speaker 3: and we speak.

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Speaker 2: With the CEO of Crypto dot com about a new

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Speaker 2: partnership with Trump Media.

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Speaker 3: So first we check in on these markets which are

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Speaker 3: under pressure from a global perspective. This is in many

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Speaker 3: ways the bond market that is a tail that wags

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Speaker 3: the dog when it comes to the equity market. Bond

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Speaker 3: markets sell off why, particularly in the UK, we're worried

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Speaker 3: about ability of governments to repay debt at the moment,

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Speaker 3: but we are seeing just a risk of tone to

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Speaker 3: this September start, and we're looking at big tech off

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Speaker 3: by one and a quarter percent. Magnificent seven last time

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Speaker 3: I checked, Ed all in the red.

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Speaker 4: In particular in video, there's.

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Speaker 2: Market sentiment and then there's news flow. In the show,

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Speaker 2: we're going to talk about Tesla three pieces data from China,

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Speaker 2: Bloomberg reporting on India launch and master Plan Part four

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Speaker 2: in Vinya. Maybe it's a post earnings thing. There is

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Speaker 2: anxiety broadly about the mag seven names and concentration risk

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Speaker 2: at the index level. And then TSMC. We just broke

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Speaker 2: the story the US pulling a key waiver for TSMC

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Speaker 2: to get key gear to a pretty much sole facility

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Speaker 2: for chip making in China. I want to get the details.

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Speaker 2: Let's bring in Bloomberg Senior editor Mike Shephard and Mike,

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Speaker 2: what's the need to know here specifically on TSMC in China.

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Speaker 5: Well, this adds at another speed bump for a TSMC,

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Speaker 5: and the supplier is in trying to bring equipment, chemicals

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Speaker 5: and other things that they need for this facility in Nanjing.

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Speaker 5: It is not a major portion of TSMC's overall manufacturing

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Speaker 5: picture accounts for a relatively small fraction of the company's revenue,

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Speaker 5: and yet it is symbolically significant because the move mirrors

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Speaker 5: with the US government announced on Friday, and that is

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Speaker 5: that Samsung and sk Heinex would face similar restrictions on

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Speaker 5: their facilities in China going forward. All of this takes

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Speaker 5: effect at the end of the year. They have until

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Speaker 5: December thirty first before these new restrictions snap back into effect.

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Speaker 5: What happened, in Essen said, is that the company's lost

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Speaker 5: what is considered really a blanket waiver of validated end

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Speaker 5: user agreement that they were trusted to be able to

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Speaker 5: ship goods and material into those manufacturing facilities in China

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Speaker 5: for chip making. That's ending, and it's part of the

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Speaker 5: US government's broader effort to restrict China's access, even if

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Speaker 5: it's foreign companies making things inside China, to restrict access

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Speaker 5: by China overall to advanced technology.

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Speaker 3: So, Mike, an additional one thousand or so licenses are

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Speaker 3: going to have to be processed every single year by

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Speaker 3: US officials.

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Speaker 4: But who does that hamper? Who is the supplier here?

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Speaker 3: Is it the I mean I think of ASML in Europe,

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Speaker 3: is that the knock on effect we see.

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Speaker 5: Here, Well, we could see a knock on effect on

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Speaker 5: those We looked into the supplier question at the end

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Speaker 5: of last week, and it will be a little bit

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Speaker 5: of a drag. It's unclear which companies will be affected

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Speaker 5: because it's not just ASML, it's even the chemical makers

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Speaker 5: and other kinds of suppliers that may face some of

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Speaker 5: those speed bumps. Now, as I mentioned the facility that

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Speaker 5: TSMC is operate in Nanjing, it is an older grade

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Speaker 5: technology there. It's sixteen nanometer chip technology that has been

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Speaker 5: out in the market care for really a decade, so

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Speaker 5: it may not be the most cutting edge of material,

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Speaker 5: but what we're looking out overall is going to be

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Speaker 5: some delays. This adds, as you noted, a thousand more

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Speaker 5: permits that may need to be in the pipeline and

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Speaker 5: approve by a government that is really understrained here in

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Speaker 5: Washington from staffing and budget cuts and may not be

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Speaker 5: as enthusiastic about issuing those kinds of approvals. We did

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Speaker 5: see at the end of last week with the Samsung

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Speaker 5: and s k Heinex announcement, an indication from the Commerce

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Speaker 5: Department that, look, these approvals are not going to go

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Speaker 5: to any sort of equipment or other supplies that may

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Speaker 5: be used to upgrade or expand capacity at those facilities,

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Speaker 5: So they are interested in keeping things where they are

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Speaker 5: rather than allowing expansion or improvement in technology of those plans.

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Speaker 3: The US versus China AI race continues Michael Sheppard, We

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Speaker 3: appreciate it. Look, let's get the wider context here of

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Speaker 3: a tech sector that is facing another day of losses.

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Speaker 3: The Magnificent seven names in particular, every single one is

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Speaker 3: in the red. Let's get over to bluem executary report

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Speaker 3: around va Selica for more in Chicago and the pressure

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Speaker 3: point here is one evaluation is it is it more

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Speaker 3: the bond market that dictates or just a desire to

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Speaker 3: be risk off at this moment.

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Speaker 6: Hey, good morning, thanks for having me. I'd say there's

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Speaker 6: probably some combination of all of those right now. I

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Speaker 6: would just add that most of these mag seven names

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Speaker 6: have been very strong performers this year, especially over the

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Speaker 6: past few months since the April low. It is a

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Speaker 6: natural place for investors to be wanting to take profits,

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Speaker 6: especially if there's any sort of broader sense of uncertainty

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Speaker 6: or any kind of risk off sediment. This is sort

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Speaker 6: of the place you might go first in order to

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Speaker 6: take profits just because of how well they've done. Now

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Speaker 6: you mentioned valuations, because of how much they've risen over

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Speaker 6: the past couple of months. We have gotten to a

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Speaker 6: point where a lot of people are starting to feel

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Speaker 6: maybe they're a little bit frothy, maybe there's some room

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Speaker 6: for consolidation. On the downside, there's a lot of sort

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Speaker 6: of sentiment about that right now.

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Speaker 2: Ryan, let's focus on in Video is down for a

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Speaker 2: full straight day, right, longest streak of decline since since March,

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Speaker 2: which on the face of it isn't really that big

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Speaker 2: a deal. But there's a lot of focus on the

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Speaker 2: terminal this morning about weighting of the S and P

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Speaker 2: five hundred and how much of a contribution in Video

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Speaker 2: alone made to the gains we've seen inequity markets this year.

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Speaker 6: So last week Nviigya came out with this report and

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Speaker 6: it was broadly positive, but you did have some questions

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Speaker 6: about China revenue. You have some questions about the sustainability

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Speaker 6: of growth right now. And I'll say that the report

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Speaker 6: was strong enough that we did see people increase their estimates,

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Speaker 6: which had the impact of reducing the forward multiple. But

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Speaker 6: I think, like you said, this is a stock that's

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Speaker 6: done very well this year. It is absolutely enormous in

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Speaker 6: terms of market cap and its weight within the major indexes.

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Speaker 6: So it does seem like a natural place for investors

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Speaker 6: to be taking some profits, especially since we're now past

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Speaker 6: the earnings catalyst and it just given the overall weight

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Speaker 6: that it has in major indexes, is kind of natural

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Speaker 6: that we are sort of feeling the trimmers of that

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Speaker 6: just across the overall equity market.

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Speaker 2: Those Ryan las Selca, thank you very much. Let's stay

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Speaker 2: with video. Anthony saglum Ben and Paraised Financial chief market

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Speaker 2: strategist joins us now and I use the phrase concentration risk, Anthony,

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Speaker 2: but there's two types of concentration risk. There's Nvidia's waiting

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Speaker 2: in key benchmark indexes, right, and then there's the concentration

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Speaker 2: risk of where in Vidias derived sales the hyperscalers which

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Speaker 2: came up in the print last week. Of those two,

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Speaker 2: which is the market most nervous about right now.

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Speaker 7: I know I would say, you know, I think it's

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Speaker 7: the letter. I think revenue risk when you heard in

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Speaker 7: video report and you kind of and I think it

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Speaker 7: gets highlighted it very well. The overall numbers were very good.

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Speaker 7: There's some obviously uncertainty about the revenue that's being generated

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Speaker 7: in China, which kind of brought some of the kind

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Speaker 7: of the data revenue down, But at the end of

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Speaker 7: the day, the overall picture for AI and in Vidia's

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Speaker 7: place in AI is very strong. But it really is

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Speaker 7: about execution. And when you kind of dig into the

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Speaker 7: numbers and you see the hyperscalers having such a dominant

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Speaker 7: role in driving their revenue, you know, mag seven companies

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Speaker 7: accounting for forty percent of their revenue or so, you

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Speaker 7: have to take a step back because if over time

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Speaker 7: investors in those companies want a little bit more payback,

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Speaker 7: they want to see return on investment. Uh, they're concerned

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Speaker 7: about the cap X that's being spent by these companies.

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Speaker 7: Well that's in Vidia's revenue, and so there is a

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Speaker 7: little bit of unease around that. But I would point

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Speaker 7: out the NANSDAC just finished August up for the fifth

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Speaker 7: straight month, and as you guys highlighted, there's been a

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Speaker 7: lot of strength in the Magnificent seven names. And so

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Speaker 7: September is wall streets hate this month or they hate

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Speaker 7: the month of September, and that's what we're kind of

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Speaker 7: seeing today. Our view is that technicals are not stretched.

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Speaker 7: There is a little bit of risk that markets could

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Speaker 7: pull back, but I do believe fundamentals for Nvidia and

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Speaker 7: technology as a whole remained very solid.

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Speaker 3: I mean, yes, Anthony was seeing in Vidio down on

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Speaker 3: two weeks, but over year to date up twenty five percent.

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Speaker 3: But the idea that you've got concentration in where the

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Speaker 3: revenue comes from for in video, and then you've got

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Speaker 3: concentration in video and those mag seven names in ultimate

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Speaker 3: rewards that every investor has felt right now, how many

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Speaker 3: people are trying to trim that overall concentration in their books.

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Speaker 7: You know, I think a lot of traders and institutional

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Speaker 7: investors probably got ahead of the seasonal factors that they

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Speaker 7: probably looked at some of the earnings or suspected that

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Speaker 7: there could be a little bit of volatility around in

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Speaker 7: video's earnings. And so I don't think there's a massive

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Speaker 7: repositioning happening around in video. But as you mentioned, it's

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Speaker 7: eight percent of the S and P five hundred. We've

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Speaker 7: seen the NASDAC and the MAGS Magnificent seven have very

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Speaker 7: strong performance since the April lows. We're in a weaker

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Speaker 7: seasonal kind of factor for the markets, and so I

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Speaker 7: think it's just natural that you're seeing a little rotation

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Speaker 7: out of big tech. I don't think that's bad for

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Speaker 7: the market overall. What we saw in August is areas

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Speaker 7: like financials and energy and materials actually did pretty well.

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Speaker 7: And when you look at the earnings expectations over the

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Speaker 7: third quarter, actual expectations for Nvidia and Big Tech and

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Speaker 7: Magnificent seven went up last month, and they actually have

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Speaker 7: gone up over the last two months. So when we

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Speaker 7: look at fundamentals, we think they're still strong in technology.

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Speaker 7: But as I said earlier, it's about execution, and at

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Speaker 7: these stretched valuations, investors sho expect a little bit more

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Speaker 7: volatility around these really concentrated names that make up a

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Speaker 7: lot of the major indexes today.

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Speaker 3: Calm Context, Anthony segum Beni, it's great to have you

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Speaker 3: on from Americ Price. Thank you kicking off the September trade. Meanwhile,

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Speaker 3: coming up Klana, it's preferring to go public after delays

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Speaker 3: earlier this year. We look at why the fintech company

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Speaker 3: seeks evaluation size fourteen billion dollars.

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Speaker 4: This is a blue back tech.

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Speaker 3: Klana and its backers are looking to raise more than

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Speaker 3: a billion dollars in a highly anticipated IPO as soon

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Speaker 3: as next week. And mex Matthew Griffin is with us,

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Speaker 3: joining us for more and a week of marketing kicks

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Speaker 3: off formally ahead of what could be September the ninth,

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Speaker 3: pricing trading September the tenth. What are the key facts

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Speaker 3: that we know about this business.

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Speaker 8: It's a really interesting road that Klara has taken to

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Speaker 8: get here. They started as a sort of comparable to

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Speaker 8: PayPal payments company. They rose to prominence during the pandemic

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Speaker 8: in buy now, pay later financing for consumer purchases, and

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Speaker 8: now just in the last few months, they're really making

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Speaker 8: a new push to become a digital bank, to offer

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Speaker 8: debit cards, to offer a bank accounts to people. So

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Speaker 8: it'll be really interesting to see which peers they end

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Speaker 8: up trading, like, who investors have in mind when they're

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Speaker 8: thinking about this company, and how each of these businesses

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Speaker 8: fit together, how that banking push goes. As we get

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Speaker 8: a little bit further out past the IPO.

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Speaker 2: I remember covering with Caro twenty twenty one that valuation

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Speaker 2: in the private market it's forty six billion or something

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Speaker 2: like that. So at the top end it's a fourteen

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Speaker 2: billion dollar evaluation. But there's a biggest story about IPO

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Speaker 2: environment this week alone. What are you saying.

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Speaker 8: We've had a really hot summer at this point. In IPOs,

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Speaker 8: I mean, just today you've got filings from Figure, they're

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Speaker 8: a blockchain based lending company. Also in financial technology, you've

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Speaker 8: got Gemini, which is a crypto exchange, and then you've

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Speaker 8: also got Legends that's an engineering and HVAC company backed

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Speaker 8: by Blackstone, which is a possible sign that the pipeline

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Speaker 8: is broadening out a little bit.

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Speaker 4: The reason that's important is, you know.

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Speaker 8: A lot of IPOs were paused during the March volatility

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Speaker 8: that we saw this spring because of tariffs. Things have

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Speaker 8: bounced back. They're looking a little bit more like what

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Speaker 8: investors hoped they'd see. But some of the first companies

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Speaker 8: we saw go were, for example, e Toro, fintech, you know,

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Speaker 8: companies that aren't directly affected by tariffs. So now we're

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Speaker 8: seeing more companies. We're seeing companies that offer physical goods

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Speaker 8: and services, and so that's potentially a thing that investors

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Speaker 8: are going to take as a sign of hope for

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Speaker 8: the pipeline this year and for a broader revival.

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Speaker 2: Right Bloomberg's Matthew Griffin, great reporting, Thank you very much. Meanwhile,

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Speaker 2: fintech firm Resolute, has kicked off a process for some

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Speaker 2: employees to sell their shares in the company at a

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Speaker 2: seventy five billion dollar valuation. That's according to a memo

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Speaker 2: seen by Bloomberg. The secondary sale, we'd value each share

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Speaker 2: at one three hundred and eighty one dollars, and sources

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Speaker 2: say staff of the firm would be able to sell

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Speaker 2: as much as twenty percent of their personal stakes Cara Significant.

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Speaker 3: Meanwhile, coming up, the CEO of Crypto dot com is

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Speaker 3: joining us. It's going to be talking about the company's

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Speaker 3: latest partnership with Trump Media and a new focus on

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Speaker 3: the growing sports predictions market. But before that, we get

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Speaker 3: back to this market that is in cell off mode.

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Speaker 3: Nazak one hundred of by one point four percent. In fact,

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Speaker 3: the worst two days sell off we've seen since April

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Speaker 3: in video off for four straight days. We haven't seen

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Speaker 3: that since March of this year. Another three percent is gone.

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Speaker 3: There's a brummeg tech. It's time now for talking tech

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Speaker 3: and first up Amazon Well, it's launched its cloud services

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Speaker 3: in New Zealand, says it will invest four point four

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Speaker 3: billion dollars in data centers in the country.

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Speaker 4: Now.

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Speaker 3: The move comes as the country is looking to attract

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Speaker 3: more foreign investment. The construction, operation and maintenance of the

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Speaker 3: data centers expected to add about one thousand full time jobs.

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Speaker 4: Plus.

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Speaker 3: Lazan has appointed Dmitri Chevalenko, the Cheap Business Officer of Perplexity,

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Speaker 3: to its port effective today now. Prior to joining Perplexity,

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Speaker 3: Chevalenko previously held senior roles ober at LinkedIn and at Meta.

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Speaker 4: But Lazard CEO Peter Orzag says that.

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Speaker 3: Dimitri's AI expertise will support its long term strategy and

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Speaker 3: cryptocurrency exchange Gemini space station, and it seeks to raise

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Speaker 3: as much as three on ore and sixteen point seven

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Speaker 3: million dollars in its IPO. The firm, of course, led

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Speaker 3: by the Winklevoss Twins, plans to market sixteen point seven

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Speaker 3: million shares seventeen to nineteen dollars each, giving the company

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Speaker 3: evaluation of some two point two billion dollars at the

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Speaker 3: top end of the range.

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Speaker 5: ED.

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Speaker 2: Let's stick with the digital currency space and bringing Chris Marslick.

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Speaker 2: He's the CEO of crypto dot Com, which last week

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Speaker 2: announced the partnership with Trump Media and a blank check

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Speaker 2: Vehicle to launch a new crypto treasury business its focus

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Speaker 2: buying Upcrypto dot COM's own token known as Chronos or Crow.

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Speaker 2: I think, Chris, it's one of those stories where we

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Speaker 2: start with the basics.

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Speaker 4: Where did the idea.

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Speaker 2: Come from to do this of a Kronos treasury company,

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Speaker 2: Where did it originate?

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Speaker 9: I think it's a little bit of a trend by

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Speaker 9: now and a pioneer and by micros MicroStrategy, and I

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Speaker 9: think every single blockchain from the top, say twenty will

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Speaker 9: have a leading treasury company that will be focusing on

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Speaker 9: driving demand for it and acquiring as much as physically possible.

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Speaker 9: And we thought that partnering with Trump Media to do

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Speaker 9: so is just the perfect match.

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Speaker 2: So there are a number of initiatives between Crypto dot

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Speaker 2: Com and Trump Media or the broader Trump family. Where

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Speaker 2: did that relationship start, Who did you first meet and

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Speaker 2: how long have you held those relationships?

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Speaker 9: So I think it comes dates back a couple of years.

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Speaker 9: And you know, we always look forward to working with

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Speaker 9: people who are procrypto, who want to help drive this

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Speaker 9: industry forward. And I think it has to be said

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Speaker 9: that both the current administration and specifically Trum Media Technology

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Speaker 9: Group has probably done it out in this space. We've

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Speaker 9: helped them exit get on their big contrast strategy with

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Speaker 9: a multi billion dollar a bit Com purchase. We help

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Speaker 9: them cussid coins, so we provide infrastructure, and this new

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Speaker 9: play on cr is just an extension of the relationship.

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Speaker 3: Extension of a relationship that broadens out to ETFs, that

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Speaker 3: broadens out to payments, a subscription creation. But it also

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Speaker 3: broadens out the conversation that people are having about more

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Speaker 3: broadly benefits to the Trump's broader family when it comes

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Speaker 3: to crypto, Chris, when you're having those conversations with friends

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Speaker 3: with family, how do you talk about whether or not

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Speaker 3: there might be any conflicts of interest?

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Speaker 4: Which I will reiterate.

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Speaker 3: The family and indeed spoke to you for the administration

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Speaker 3: would say there are none.

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Speaker 9: And you know you have to agree with their statements

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Speaker 9: because everything is held in brid trusts and this is

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Speaker 9: a published company that operates independently, and you know, we

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Speaker 9: are still a privately held company. But we love partnering

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Speaker 9: with people who to really take this industry forward. So

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Speaker 9: I think people need to understand that the administration has

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Speaker 9: set a certain agenda which places cryptocurrency industry at the

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Speaker 9: very center of it. With setting out a very ambitious

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Speaker 9: role for America in this space, and anything we can

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Speaker 9: do to help, we'll do so we'll support it.

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Speaker 3: What's interesting is you say you're still privately held. What

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Speaker 3: are the plans of crypto dot com? More broadly, we're

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Speaker 3: just hearing of IPOs of Gemini and the like. Would

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Speaker 3: you ever think of an IPO? Would you ever think

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Speaker 3: about selling parts of the business?

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Speaker 9: Right? I have to admit it's quite tempting to consider

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Speaker 9: these options, given how richly the market values crypto companies

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Speaker 9: these days. We certainly have the numbers to do so.

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Speaker 9: We've done about one point five billion in revenue last year,

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Speaker 9: about a billion in gross profit, reinvested about seven hundred

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Speaker 9: million of it. So let's say a free had a

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Speaker 9: million net profitability this year. I think it's going to

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Speaker 9: be better, especially if we see the Federate cut and

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Speaker 9: a strong Q four following. So we have the numbers,

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Speaker 9: and we've been approached by all the top names in

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Speaker 9: them of the investment banks. We want to be a

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Speaker 9: very well run company, so we are working on preparing everything,

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Speaker 9: but no decisions have been made at this point. We

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Speaker 9: think that we actually enjoy operating as a private company.

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Speaker 9: It alls us to move really fast. We've got a solid,

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Speaker 9: solid baland street, so we don't have to make any

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Speaker 9: decisions in a short term.

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Speaker 2: Some breaking news this morning that you're looking at starting

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Speaker 2: sports prediction markets, particularly for the NFL. That's so interesting.

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Speaker 2: There was market reaction from some of the established players

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Speaker 2: that in that space. Right, explain how it's going to get.

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Speaker 4: It's going to work, but why.

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Speaker 9: Look, we think that prediction markets are going to be huge,

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Speaker 9: and sports is a part of it, but it's not

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Speaker 9: the whole thing. And if there is any company out there,

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Speaker 9: a large business that wants to build out a prediction

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Speaker 9: market operation, we are the perfect infrastructure partner. We are

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Speaker 9: regulated by the CFTC. We've got battle tested trading technology,

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Speaker 9: very robust, APIs all the best market makers. We want

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Speaker 9: to be the liquidity center for prediction markets on shore

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Speaker 9: in the US. So we'll play very aggressively in that space.

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Speaker 3: And you're someone who's comfortable perhaps in markets that regulation

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Speaker 3: is always trying to catch up with, Chris, it's interesting

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Speaker 3: that the CFDC federal courts are still debating whether, ultimately

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Speaker 3: sports prediction.

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Speaker 4: Markets counter is gambling. How do you think about the

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Speaker 4: regulation process? Having given what you've lived through when it

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Speaker 4: comes to crypto.

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Speaker 9: I think it's news space and it's going to evolve,

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Speaker 9: and the regulatory setup is going to have to evolve

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Speaker 9: with it. We've gone through this journey with cryptocurrency. We

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Speaker 9: are used to being the role of being in the

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Speaker 9: role of a trusted counselor to regulators and helping them

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Speaker 9: understand how they can do their job effectively while allowing

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Speaker 9: market participants to make use of these wonderful instruments.

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Speaker 2: Just very quick, Chris, do you expect that this could

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Speaker 2: be some sort of meaningful contribution to revenues this year?

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Speaker 2: You kindly gave us your financials for last year. Just

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Speaker 2: model it out for us.

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Speaker 9: I think it's still it's not going to be a

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Speaker 9: massive contributor this year, but we're also very aggressive and

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Speaker 9: pricing for our partners, Like it's all about building liquidity

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Speaker 9: right now. But if you look at what's going to

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Speaker 9: be in like five years time, I think it's going

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Speaker 9: to be a massive business line for US.

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Speaker 3: Crypto dot com CEE Chris Mazlek is great speaking with you.

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Speaker 3: Coming against meanwhile coming up Tesla well in underwhelms and

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Speaker 3: it's India debut, so much more news on that particular company,

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Speaker 3: but we are more broadly looking at Tesla shares by

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Speaker 3: one and a half percent. In fact, ed every single

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Speaker 3: Magnificent seven name is in the red today. We are

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Speaker 3: looking at a market that is under pressure as we

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Speaker 3: head into a pretty tumultuous month of September. Checking out

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Speaker 3: mag seven off by more than two percent ed.

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Speaker 2: Yeah, the Tesla one's so interesting, Like there's three stories

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Speaker 2: out there Bloomberg breaking the numbers on what's a pretty

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Speaker 2: soft launch for them in India. But again there's this

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Speaker 2: like macro level, macro level. Yeah, it's a macro level thing.

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Speaker 2: Anxiety about how much concentration risk there is right now,

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Speaker 2: particularly within Vidia. We've had some great conversations about that,

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Speaker 2: our earlier guest saying that September the most hated month.

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Speaker 10: Carol.

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Speaker 4: Notable.

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Speaker 3: Whether we'll see or not some of the pressure continue

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Speaker 3: on in video. Is so good to have your voice

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Speaker 3: back on this show as we enter the month of September.

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Speaker 3: Four straight days of glosses for the company. This is

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Speaker 3: Bloomberg Tech.

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Speaker 2: Welcome back to Bloomberg Tech, and we'll get right to

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Speaker 2: markets and right to video. It's kind of the biggest

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Speaker 2: drag right now, down for a fourth straight session, which

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Speaker 2: is the biggest run of decline since March, which actually,

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Speaker 2: let's be honest, it's not much to right home about

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Speaker 2: other than we've just had a long weekend following in

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Speaker 2: Nvidia's earnings print which was last Wednesday. There's this macro

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Speaker 2: level anxiety of concentration risk because of Nvidia's waiting and

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Speaker 2: other magsv waitings. At the index level, there's still some

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Speaker 2: kind of unpicking of what happened at earnings. And thankfully

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Speaker 2: for US, Bloomberg's Ian King, who has led semiicinducts of

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Speaker 2: coverage at this company since the nineties, joins US onset

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Speaker 2: and that's the thing. I don't think anything's really changed

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Speaker 2: since Wednesday or Thursday of last week, but the market

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Speaker 2: is moving in that general downward direction.

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Speaker 11: Yeah. I mean, their earnings was kind of a non

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Speaker 11: event in many respects. There was a lot of left

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Speaker 11: in the kind of TBD category. We still really don't

436
00:23:53,320 --> 00:23:55,800
Speaker 11: know what's going to happen with China, when that revenue

437
00:23:55,800 --> 00:23:57,399
Speaker 11: is going to come back, how much of it is

438
00:23:57,440 --> 00:24:00,919
Speaker 11: going to come back, but we believe probably will be

439
00:24:00,960 --> 00:24:03,000
Speaker 11: some China revenue. So that was a kind of a

440
00:24:03,040 --> 00:24:06,399
Speaker 11: mixed message. We know that Invidia is still concentrated on

441
00:24:06,440 --> 00:24:10,480
Speaker 11: a few extremely big customers, but again nothing surprising there,

442
00:24:10,480 --> 00:24:13,240
Speaker 11: and fundamental demand, as they said, was okay.

443
00:24:13,320 --> 00:24:16,240
Speaker 3: And in though the broader context that leans into the

444
00:24:16,320 --> 00:24:19,880
Speaker 3: China story is again another headline that hits TSMC's ability

445
00:24:19,920 --> 00:24:22,040
Speaker 3: to get chip equipment, for example, in the country. So

446
00:24:22,280 --> 00:24:25,600
Speaker 3: the US China tension not to mention, of course China

447
00:24:26,080 --> 00:24:31,040
Speaker 3: really leaning into its own domestic creative creativity, whether it's

448
00:24:31,080 --> 00:24:34,200
Speaker 3: innovation on chip design or indeed manufacturing, is that speaking

449
00:24:34,280 --> 00:24:35,680
Speaker 3: to some of this weakness.

450
00:24:36,640 --> 00:24:38,959
Speaker 11: It could possibly, I mean, what it's reminding us of,

451
00:24:39,200 --> 00:24:41,080
Speaker 11: as if we could forget, is that this is a

452
00:24:41,160 --> 00:24:45,240
Speaker 11: very volatile situation and that the trend is towards decoupling,

453
00:24:45,240 --> 00:24:47,880
Speaker 11: that China goes its own way and tries to wean

454
00:24:47,960 --> 00:24:51,880
Speaker 11: itself off American technology. That's something that's been happening and

455
00:24:51,960 --> 00:24:54,040
Speaker 11: this kind of event is a reminder of that and

456
00:24:54,080 --> 00:24:56,760
Speaker 11: obviously long term that is not good for in video

457
00:24:56,960 --> 00:24:58,879
Speaker 11: or any other US technology.

458
00:24:58,920 --> 00:25:01,879
Speaker 2: Stop with Rsie and King, thank you very much, just

459
00:25:02,000 --> 00:25:05,919
Speaker 2: turn to Tesla. Loads of news out on Tesla China

460
00:25:06,040 --> 00:25:10,719
Speaker 2: data month or month change, year and year drop customers

461
00:25:10,800 --> 00:25:14,040
Speaker 2: switching to local rivals. Tesla struggling elshere in Asia. It's

462
00:25:14,119 --> 00:25:17,960
Speaker 2: Indian order numbers disappointing investors, a story that Bloomberg broke.

463
00:25:18,200 --> 00:25:21,399
Speaker 2: And then Elon Musk is downplaying the car business. He

464
00:25:21,440 --> 00:25:25,120
Speaker 2: says Tesla will derive eighty percent of its value from

465
00:25:25,160 --> 00:25:28,560
Speaker 2: the Optimus robot, the still in development. A trio of stories.

466
00:25:28,600 --> 00:25:32,040
Speaker 2: Bloomberg's Craig Trudell joins us from London. Actually, I want

467
00:25:32,040 --> 00:25:34,479
Speaker 2: to start with the India piece of it if we can.

468
00:25:34,560 --> 00:25:37,240
Speaker 2: That's a story that Bloomberg broke, and we have specific

469
00:25:37,320 --> 00:25:41,560
Speaker 2: numbers about initial orders that India has for Tesla. Just

470
00:25:41,600 --> 00:25:42,480
Speaker 2: explain that to us.

471
00:25:42,480 --> 00:25:46,760
Speaker 12: Great, Yeah, just a little over six hundred orders at

472
00:25:46,760 --> 00:25:49,040
Speaker 12: this point, and you know, just to put that number

473
00:25:49,200 --> 00:25:54,679
Speaker 12: into perspective, Tesla delivered roughly that number of vehicles around

474
00:25:54,720 --> 00:25:57,959
Speaker 12: every four hours in the first half, so really really tiny.

475
00:25:58,080 --> 00:26:01,080
Speaker 12: And this is not necessarily a show, right. India is

476
00:26:01,080 --> 00:26:04,720
Speaker 12: a very price price conscious, price sensitive market. There's not

477
00:26:04,800 --> 00:26:07,880
Speaker 12: a lot of demand for more expensive vehicles, and part

478
00:26:07,920 --> 00:26:09,720
Speaker 12: of that, of course has to do with you know,

479
00:26:09,840 --> 00:26:15,199
Speaker 12: tax treatment and tariffs. Tesla's getting some relief from that perspective,

480
00:26:15,240 --> 00:26:18,159
Speaker 12: but not a whole lot. And you know, I do

481
00:26:18,240 --> 00:26:21,280
Speaker 12: think that even with the the sort of relief being

482
00:26:21,840 --> 00:26:25,320
Speaker 12: you know, only so helpful, I think there was still

483
00:26:25,359 --> 00:26:31,240
Speaker 12: some hope or some you know, prayers that maybe you know,

484
00:26:31,320 --> 00:26:34,399
Speaker 12: India would be a source of growth for this company company,

485
00:26:34,440 --> 00:26:36,560
Speaker 12: and it's looking like, you know, a pretty slow start

486
00:26:36,560 --> 00:26:37,040
Speaker 12: for them there.

487
00:26:37,080 --> 00:26:38,879
Speaker 4: I meanwhile, China is not source of growth.

488
00:26:38,920 --> 00:26:41,919
Speaker 3: We've seen yet another dismal monthly numbers in terms of

489
00:26:41,920 --> 00:26:45,240
Speaker 3: four percent lower in terms of units being shipped, not

490
00:26:45,359 --> 00:26:49,200
Speaker 3: to mention Europe's number last week, which was very ugly. Craig,

491
00:26:49,320 --> 00:26:51,560
Speaker 3: all of this just speaks to an ongoing weakness in

492
00:26:51,640 --> 00:26:52,600
Speaker 3: car sales or Tesla.

493
00:26:53,960 --> 00:26:56,640
Speaker 12: Yeah, and I do think, you know, the US, of course,

494
00:26:56,680 --> 00:26:59,960
Speaker 12: will be very interesting to see over the next you know,

495
00:27:00,000 --> 00:27:03,159
Speaker 12: a few weeks. We'll get quarterly deliveries at the beginning

496
00:27:03,200 --> 00:27:06,840
Speaker 12: of next month. I think everyone is, of course counting

497
00:27:06,840 --> 00:27:09,280
Speaker 12: on there being a big pull ahead of people in

498
00:27:09,359 --> 00:27:12,840
Speaker 12: America wanting to take advantage of the tax credits before

499
00:27:12,840 --> 00:27:16,040
Speaker 12: they go away, and Tesla really kind of utilizing that

500
00:27:16,080 --> 00:27:18,439
Speaker 12: for all it's worth. But the big question for me

501
00:27:18,560 --> 00:27:20,639
Speaker 12: is what's on the other side of that. And also,

502
00:27:21,119 --> 00:27:24,119
Speaker 12: you know, even even if we see you know, a

503
00:27:24,200 --> 00:27:27,400
Speaker 12: strong US number, will it be offset by the fact that,

504
00:27:27,480 --> 00:27:30,080
Speaker 12: you know, the China slow down is substantial and the

505
00:27:30,119 --> 00:27:33,720
Speaker 12: europe you know, sales. I think it's safe to refer

506
00:27:33,760 --> 00:27:36,119
Speaker 12: to them as an out and out collapse at this point.

507
00:27:36,960 --> 00:27:40,080
Speaker 2: Craig Tesla's published Master Plan Part four in the last

508
00:27:40,119 --> 00:27:42,119
Speaker 2: twenty four hours. But the bit we're paying attention to

509
00:27:42,920 --> 00:27:45,600
Speaker 2: was some commentary from Mila Musk on X about the

510
00:27:45,640 --> 00:27:48,800
Speaker 2: future of the business. And it's not cars or robotaxis,

511
00:27:49,080 --> 00:27:50,320
Speaker 2: it's humanoid robots.

512
00:27:51,760 --> 00:27:54,520
Speaker 12: Yeah, and I do wonder how much of this is

513
00:27:54,760 --> 00:27:57,840
Speaker 12: you know, an effort on Musk's part to kind of

514
00:27:58,520 --> 00:28:01,800
Speaker 12: paper over and distract from from how much of a

515
00:28:01,880 --> 00:28:04,879
Speaker 12: challenge they're having on the car side. And the question,

516
00:28:04,960 --> 00:28:08,480
Speaker 12: of course, with this potential for optimist is when is

517
00:28:08,520 --> 00:28:10,679
Speaker 12: this thing actually going to be ready for you know,

518
00:28:11,440 --> 00:28:15,800
Speaker 12: actual revenue generation, you know, when anyone's been able to

519
00:28:15,840 --> 00:28:18,520
Speaker 12: pin Elon down on a sort of rough sense of

520
00:28:18,560 --> 00:28:21,280
Speaker 12: a date. He talked back in January about maybe the

521
00:28:21,359 --> 00:28:24,199
Speaker 12: second half of next year. This thing is, you know,

522
00:28:24,280 --> 00:28:29,040
Speaker 12: effective for really cool videos, for little demonstrations, but in

523
00:28:29,600 --> 00:28:32,560
Speaker 12: Elon terms, you know, this thing is not necessarily doing

524
00:28:32,600 --> 00:28:35,480
Speaker 12: something useful yet, And until and unless we see that,

525
00:28:35,560 --> 00:28:37,480
Speaker 12: it's really hard to kind of take him at his

526
00:28:37,520 --> 00:28:39,760
Speaker 12: word that this valuation of the company is going to

527
00:28:39,760 --> 00:28:42,600
Speaker 12: be tied to something that is still just an internal

528
00:28:42,600 --> 00:28:43,720
Speaker 12: developmental project.

529
00:28:43,720 --> 00:28:47,000
Speaker 3: At this point, we're watching those little cool videos as

530
00:28:47,040 --> 00:28:50,120
Speaker 3: you're speaking. Creatudell, Thanks for breaking down all of those

531
00:28:50,160 --> 00:28:53,320
Speaker 3: stories for us. Let's keep the conversation going because Pierre

532
00:28:53,360 --> 00:28:55,760
Speaker 3: Farragho is with US New Street Research had a global

533
00:28:55,760 --> 00:28:58,080
Speaker 3: tech infrastructure. Who has I think it is four hundred

534
00:28:58,080 --> 00:29:01,240
Speaker 3: and sixty five price target on Tesla. Clearly see forty

535
00:29:01,280 --> 00:29:04,840
Speaker 3: percent upside from here. But when you're hearing the master

536
00:29:04,880 --> 00:29:08,560
Speaker 3: Plan part four, are you sing optimists could really be

537
00:29:08,600 --> 00:29:09,880
Speaker 3: eighty percent of revenue?

538
00:29:10,520 --> 00:29:14,480
Speaker 13: Well, optimist is about you know, creating like humanoid robots

539
00:29:14,480 --> 00:29:18,640
Speaker 13: that could take billions of jobs on the planet. So

540
00:29:18,680 --> 00:29:21,920
Speaker 13: as long as you remain vague on timeline and what

541
00:29:22,040 --> 00:29:24,800
Speaker 13: the scenario looks like, yes, it's very easy to imagine

542
00:29:24,840 --> 00:29:31,920
Speaker 13: that Tesla would be first like extended in terms of

543
00:29:32,000 --> 00:29:35,720
Speaker 13: valuation with like a successful robot taxi operation, and then

544
00:29:35,720 --> 00:29:39,680
Speaker 13: of course like a fleet of like hundreds of millions

545
00:29:39,680 --> 00:29:43,360
Speaker 13: of human in robots with dwarf Actually, is the economic

546
00:29:43,480 --> 00:29:46,800
Speaker 13: value of even a robot taxi business. So you have

547
00:29:46,840 --> 00:29:49,880
Speaker 13: to remember that you know this this planned Tesla publishes

548
00:29:49,960 --> 00:29:53,280
Speaker 13: and even likes to comment on, is really like a vision,

549
00:29:53,880 --> 00:30:00,600
Speaker 13: like a stretch vision. Is always been managing managing his businesses,

550
00:30:01,240 --> 00:30:03,240
Speaker 13: bringing up like the very long term visions, and he

551
00:30:03,360 --> 00:30:07,120
Speaker 13: has his way of presented presenting these versions very often

552
00:30:07,160 --> 00:30:11,200
Speaker 13: with like a slightly distorted time timescale, you know when

553
00:30:11,200 --> 00:30:14,120
Speaker 13: he talks about like mass and the conquest of mass

554
00:30:14,160 --> 00:30:16,320
Speaker 13: and the SpaceX and things like that. So you have

555
00:30:16,400 --> 00:30:19,760
Speaker 13: to put the comments in the right context. So today

556
00:30:20,720 --> 00:30:23,520
Speaker 13: we know that the robot taxi business in the most

557
00:30:23,520 --> 00:30:26,640
Speaker 13: boutish case which should be test like in scale with

558
00:30:26,760 --> 00:30:31,040
Speaker 13: robot taxes, doesn't really have significant competition. Given like the

559
00:30:32,600 --> 00:30:35,720
Speaker 13: quality of their cost base, that business would be worth

560
00:30:35,840 --> 00:30:38,239
Speaker 13: multiple trillions than would be already like a multiple as

561
00:30:38,280 --> 00:30:42,080
Speaker 13: the existing motor business. And yes, on top of that,

562
00:30:42,480 --> 00:30:46,680
Speaker 13: a human robot bull case where Tesla dominates the space

563
00:30:46,720 --> 00:30:50,480
Speaker 13: again and human aid robots can take over hundreds of

564
00:30:50,560 --> 00:30:53,160
Speaker 13: millions of jobs, you can easily get like you know,

565
00:30:53,280 --> 00:30:56,800
Speaker 13: to like the large number of trillions, like close to

566
00:30:56,800 --> 00:31:01,040
Speaker 13: ten trillion dollars of valuation. This is what I have

567
00:31:01,160 --> 00:31:03,720
Speaker 13: in my model and what you should bang on beck

568
00:31:03,840 --> 00:31:04,360
Speaker 13: On today.

569
00:31:04,440 --> 00:31:04,800
Speaker 8: Maybe not.

570
00:31:06,920 --> 00:31:10,160
Speaker 2: I spent a lot of time reading the document. It's

571
00:31:10,200 --> 00:31:13,640
Speaker 2: like an economic theory, right, they're basically arguing that because

572
00:31:13,680 --> 00:31:17,440
Speaker 2: Tesla can scale, if they do build all of these things,

573
00:31:17,680 --> 00:31:20,240
Speaker 2: there will be this sort of great economic impact around

574
00:31:20,280 --> 00:31:23,560
Speaker 2: the world to people of all class economic classes. Is

575
00:31:23,560 --> 00:31:26,440
Speaker 2: that something pair that is it analyst covering a specific

576
00:31:26,520 --> 00:31:29,040
Speaker 2: name or specific stock that you kind of model for

577
00:31:29,120 --> 00:31:31,200
Speaker 2: you go, okay, in the future, Tesla is going to

578
00:31:31,280 --> 00:31:32,760
Speaker 2: change the world economy as we know it.

579
00:31:34,440 --> 00:31:36,520
Speaker 13: Yeah, it's a very good it's a great question ed.

580
00:31:37,040 --> 00:31:39,960
Speaker 13: So let's look at you know, like what is tangible

581
00:31:39,960 --> 00:31:42,120
Speaker 13: at Tesla today is that's EOTO business, And let's look

582
00:31:42,120 --> 00:31:44,720
Speaker 13: at what good and is do covering Tesla. You understand

583
00:31:44,760 --> 00:31:50,040
Speaker 13: teslas technological you know leadership, you know, how early is

584
00:31:50,040 --> 00:31:52,240
Speaker 13: it coming to the market, is what kind of deformance,

585
00:31:52,280 --> 00:31:55,080
Speaker 13: what kind of features? And then you measure sort of

586
00:31:55,080 --> 00:31:59,360
Speaker 13: the deformance of Tesla's course based like the cost efficiency.

587
00:32:00,320 --> 00:32:03,080
Speaker 13: And so that's what we've done over the last five

588
00:32:03,160 --> 00:32:05,280
Speaker 13: years looking at Tesla and what you're seeing that Tesla

589
00:32:05,320 --> 00:32:08,280
Speaker 13: came into the market five years ago in large scale

590
00:32:09,160 --> 00:32:12,719
Speaker 13: with really like a cosway that was not that nobody

591
00:32:12,720 --> 00:32:15,600
Speaker 13: could approach, and also with like a performance, like a

592
00:32:15,680 --> 00:32:20,200
Speaker 13: quality of innovations that nobody could approach today. As you

593
00:32:20,240 --> 00:32:23,680
Speaker 13: see in China, local Chinese competitors are actually capable of

594
00:32:23,720 --> 00:32:28,120
Speaker 13: being very competitive with Tesla in terms of innovation and

595
00:32:28,160 --> 00:32:30,280
Speaker 13: in terms of cost as well. We've done very detailed

596
00:32:30,280 --> 00:32:35,680
Speaker 13: analysis of the cost of manufacturing a car, bid and

597
00:32:35,760 --> 00:32:38,479
Speaker 13: others versus Tesla, and what you see that they are

598
00:32:38,520 --> 00:32:42,560
Speaker 13: a bass on par so that type of analytical where

599
00:32:42,600 --> 00:32:44,240
Speaker 13: you can do it on a business that is actually

600
00:32:44,360 --> 00:32:47,480
Speaker 13: ramping today if you look at it like in the

601
00:32:47,520 --> 00:32:50,080
Speaker 13: longer term, it's more difficult to do. But what you

602
00:32:50,160 --> 00:32:54,880
Speaker 13: can get from Tesla is very interesting perspective that by

603
00:32:54,960 --> 00:32:59,320
Speaker 13: having a very very proactive and very integrated model and vision,

604
00:33:00,040 --> 00:33:05,360
Speaker 13: they can actually hit technological and innovation leadership with by

605
00:33:05,440 --> 00:33:08,920
Speaker 13: far the most advanced cause base, which is exactly where

606
00:33:08,920 --> 00:33:11,520
Speaker 13: they are today on the robot Taxta front. They have

607
00:33:11,640 --> 00:33:16,680
Speaker 13: the cheapest platform and the platform that today is you know,

608
00:33:16,920 --> 00:33:19,320
Speaker 13: the jury is still slightly out, but probably like the

609
00:33:19,560 --> 00:33:23,160
Speaker 13: highest platforming platform as well. And then of course Ilan's

610
00:33:23,160 --> 00:33:25,520
Speaker 13: bet is to put Tesla in the same position with

611
00:33:25,600 --> 00:33:27,680
Speaker 13: the human and robot into three years from now.

612
00:33:27,880 --> 00:33:29,680
Speaker 4: Peah, you mentioned China several times.

613
00:33:29,880 --> 00:33:32,360
Speaker 3: I just want to broad out out the conversation briefly.

614
00:33:32,680 --> 00:33:35,680
Speaker 3: When you're looking at TSMC being limited for equipment can

615
00:33:35,680 --> 00:33:38,920
Speaker 3: get in. You cover KLA, I see you cover Tokyo Electron,

616
00:33:39,000 --> 00:33:41,840
Speaker 3: you cover aland research. How much these companies can be

617
00:33:41,880 --> 00:33:43,320
Speaker 3: impacted by US China.

618
00:33:45,240 --> 00:33:49,400
Speaker 13: Well, they're all going to you know, be sitting on

619
00:33:49,440 --> 00:33:53,120
Speaker 13: their hands waiting for like directions of how the negotiation

620
00:33:53,280 --> 00:33:56,040
Speaker 13: and the relationship between the US and China is going

621
00:33:56,080 --> 00:34:00,480
Speaker 13: to be is going to be handled. If you take

622
00:34:00,520 --> 00:34:04,760
Speaker 13: specifically like semicap equipment players, they are generating a lot

623
00:34:04,760 --> 00:34:07,000
Speaker 13: of their revenue still both twenty percent of their revenues

624
00:34:07,120 --> 00:34:09,040
Speaker 13: is coming from China today and to us it's an

625
00:34:09,040 --> 00:34:13,120
Speaker 13: overhand it's a concern. It's probably coming down normalizing over time.

626
00:34:13,160 --> 00:34:15,800
Speaker 13: So we see that as a potential, you know, headwinds

627
00:34:16,800 --> 00:34:20,000
Speaker 13: on their financial performance. But you have to take things

628
00:34:20,000 --> 00:34:22,200
Speaker 13: back to where they are. It's only twenty percent of

629
00:34:22,680 --> 00:34:27,719
Speaker 13: their business. Yeah, that's kind of like a peakish at

630
00:34:27,760 --> 00:34:30,919
Speaker 13: a peak, so it's not like a game changer for them.

631
00:34:31,040 --> 00:34:33,879
Speaker 3: Of course, Pierre fair Good giving us honest on what's

632
00:34:33,920 --> 00:34:36,000
Speaker 3: game changing in China New Street Research.

633
00:34:36,320 --> 00:34:37,279
Speaker 4: We so appreciate it.

634
00:34:37,360 --> 00:34:39,719
Speaker 3: Meanwhile, coming up and recent Horror It's partner Olivia Moore

635
00:34:39,880 --> 00:34:43,320
Speaker 3: joins us discuss the top one hundred gen AI consumeer apps.

636
00:34:43,600 --> 00:34:45,719
Speaker 4: Let's see how many you're using the supreme bak tack.

637
00:34:54,640 --> 00:34:57,239
Speaker 3: And recent Horowitz has again released it's top one hundred

638
00:34:57,280 --> 00:35:00,640
Speaker 3: Jannit of AI consumer apps, fifty AI first Web products

639
00:35:00,640 --> 00:35:02,160
Speaker 3: at fifty top AI.

640
00:35:01,760 --> 00:35:02,960
Speaker 4: First Mobile apps.

641
00:35:03,400 --> 00:35:05,800
Speaker 3: And for that to discuss, we have Olivia Moore joining

642
00:35:05,840 --> 00:35:08,760
Speaker 3: a sixteen Z partner on the consumer team focused on AI.

643
00:35:08,840 --> 00:35:11,600
Speaker 3: We are so thankful because like many in the world,

644
00:35:11,760 --> 00:35:14,319
Speaker 3: we are a wash with the latest, greatest Jenai app

645
00:35:14,320 --> 00:35:16,120
Speaker 3: that we should download and when we should use it.

646
00:35:16,520 --> 00:35:17,440
Speaker 4: What's interesting with.

647
00:35:17,440 --> 00:35:20,680
Speaker 3: Your list is that we're starting to see basically stability.

648
00:35:20,880 --> 00:35:23,080
Speaker 4: We're not seeing a whole host of new names. Each

649
00:35:23,080 --> 00:35:24,879
Speaker 4: iteration app exactly yeah.

650
00:35:24,920 --> 00:35:27,160
Speaker 10: On this version of the list, we only had eleven

651
00:35:27,239 --> 00:35:29,759
Speaker 10: new names of the fifty on web, which was a

652
00:35:29,800 --> 00:35:32,000
Speaker 10: real marked divergence from the last list when it was

653
00:35:32,040 --> 00:35:35,279
Speaker 10: seventeen new names on web. And perhaps most striking to me,

654
00:35:35,400 --> 00:35:37,560
Speaker 10: we've done this list five times now we do it

655
00:35:37,600 --> 00:35:40,400
Speaker 10: every six months, and there were fourteen names that have

656
00:35:40,440 --> 00:35:43,160
Speaker 10: made every single list, which for the fact that we're

657
00:35:43,200 --> 00:35:45,440
Speaker 10: only two years into AI means that we're starting to

658
00:35:45,440 --> 00:35:46,960
Speaker 10: see some really exciting stability.

659
00:35:47,760 --> 00:35:50,560
Speaker 2: Olivia, mister Ela Musk has put in the spotlight recently

660
00:35:50,680 --> 00:35:53,640
Speaker 2: app store methodology, so to the avoidance of any doubt,

661
00:35:53,640 --> 00:35:56,680
Speaker 2: which you just go through the methodology of how Andrees

662
00:35:56,719 --> 00:35:59,279
Speaker 2: and Horowitz has put this list together for the fifth time.

663
00:35:59,320 --> 00:36:02,160
Speaker 10: I believe, Yeah, it's all objective data. So for our

664
00:36:02,200 --> 00:36:04,960
Speaker 10: web list, we use a provider called similar Web. We

665
00:36:05,080 --> 00:36:08,560
Speaker 10: rank every single website globally by the number of monthly visits,

666
00:36:08,680 --> 00:36:10,760
Speaker 10: and then we take the first fifty that are generative

667
00:36:10,760 --> 00:36:13,239
Speaker 10: AI native. And then on the mobile app side, we

668
00:36:13,320 --> 00:36:15,960
Speaker 10: use another provider called sensor Tower. We rank them by

669
00:36:16,080 --> 00:36:18,560
Speaker 10: monthly active users, and we take the first fifty again

670
00:36:18,600 --> 00:36:19,480
Speaker 10: that are AI native.

671
00:36:20,480 --> 00:36:23,360
Speaker 2: So there are some takeaways. Right chat GPT still dominates,

672
00:36:23,360 --> 00:36:24,600
Speaker 2: as you can see on the left hand side of

673
00:36:24,600 --> 00:36:27,640
Speaker 2: the screen, Google is making progress. The way I explained

674
00:36:27,640 --> 00:36:29,880
Speaker 2: this to Caroline this morning and how I approach it

675
00:36:29,960 --> 00:36:33,680
Speaker 2: is I'm starting to see these tools like streaming subscriptions.

676
00:36:34,239 --> 00:36:36,640
Speaker 2: I use all of them, some of them I pay for.

677
00:36:36,680 --> 00:36:39,719
Speaker 2: Bloomberg gives me a corporate access to chat GPT. Some

678
00:36:39,800 --> 00:36:42,160
Speaker 2: of them are free, but at some point I've got

679
00:36:42,200 --> 00:36:44,719
Speaker 2: to decide which I don't want any more. Does the

680
00:36:44,800 --> 00:36:47,920
Speaker 2: data show any of that, like short term use moving

681
00:36:47,960 --> 00:36:48,400
Speaker 2: to others.

682
00:36:49,040 --> 00:36:51,800
Speaker 10: Yeah, it's really interesting. There's a lot of cross app usage.

683
00:36:51,920 --> 00:36:54,719
Speaker 10: Quite a few general LM products made the list, and

684
00:36:54,760 --> 00:36:57,000
Speaker 10: you might expect that consumers would pick one of these,

685
00:36:57,080 --> 00:37:01,280
Speaker 10: like chat GBT or Perplexity or claud or pep seek instead.

686
00:37:01,280 --> 00:37:03,400
Speaker 10: What we're seeing in the early days is consumers are

687
00:37:03,480 --> 00:37:06,600
Speaker 10: using all of them, but maybe for different reasons every time.

688
00:37:07,280 --> 00:37:09,920
Speaker 10: But we're also seeing the emergence of things like consumer

689
00:37:09,960 --> 00:37:12,640
Speaker 10: subscriptions that cost two hundred dollars a month to use

690
00:37:12,680 --> 00:37:15,520
Speaker 10: the best version of CHATCHYBT in Perplexity, and so for

691
00:37:15,640 --> 00:37:17,880
Speaker 10: moving towards that version of the world, we might expect

692
00:37:17,920 --> 00:37:19,879
Speaker 10: to see them have to make a choice, so you're

693
00:37:19,880 --> 00:37:22,360
Speaker 10: not paying thousands and thousands of dollars per month across

694
00:37:22,360 --> 00:37:23,520
Speaker 10: your AI subscriptions.

695
00:37:23,719 --> 00:37:27,279
Speaker 3: So blend this with a sixteen z's own perspective here

696
00:37:27,520 --> 00:37:31,520
Speaker 3: on whether there will be an NLM to rule them all,

697
00:37:31,680 --> 00:37:34,840
Speaker 3: whether we'll all end up defaulting to ultimately CHATCHYBT, or

698
00:37:34,880 --> 00:37:38,080
Speaker 3: whether it's Gemini or Glock whether actually we will be

699
00:37:38,160 --> 00:37:40,719
Speaker 3: specific and we will see that there are lanes for

700
00:37:40,840 --> 00:37:43,279
Speaker 3: different apps for different use cases and they will have

701
00:37:43,360 --> 00:37:45,480
Speaker 3: different benefits.

702
00:37:45,560 --> 00:37:45,799
Speaker 4: Yeah.

703
00:37:46,440 --> 00:37:48,719
Speaker 10: I think the interesting thing about this list is CHATCHYBT

704
00:37:48,880 --> 00:37:51,600
Speaker 10: is definitely in the lead. So the number two Gemini

705
00:37:51,680 --> 00:37:54,319
Speaker 10: has about twelve percent of the traffic on web, so

706
00:37:54,360 --> 00:37:56,319
Speaker 10: it's a very big drop off between number one and

707
00:37:56,400 --> 00:37:59,319
Speaker 10: number two. But there's also an incredibly long tail here.

708
00:37:59,360 --> 00:38:01,720
Speaker 10: There's app on this list that have never raised funding.

709
00:38:01,760 --> 00:38:04,560
Speaker 10: There's apps on this list that have millions of users

710
00:38:04,600 --> 00:38:07,200
Speaker 10: but maybe do something as specific as removing a background

711
00:38:07,239 --> 00:38:10,520
Speaker 10: from a photo or generating a PowerPoint presentation. So I

712
00:38:10,520 --> 00:38:12,759
Speaker 10: think our view as a firm is that we have

713
00:38:13,000 --> 00:38:15,520
Speaker 10: the best models in the world from OPENINGI, Google and

714
00:38:15,560 --> 00:38:18,799
Speaker 10: many other companies that they're making available through API. Some

715
00:38:18,840 --> 00:38:21,560
Speaker 10: of them are even being open sourced, and that allows

716
00:38:21,560 --> 00:38:25,279
Speaker 10: companies and developers that are more opinionated about products to

717
00:38:25,320 --> 00:38:29,120
Speaker 10: build things that will serve customers for more specific use cases,

718
00:38:29,160 --> 00:38:32,080
Speaker 10: and in many cases those customers are probably more likely

719
00:38:32,120 --> 00:38:34,840
Speaker 10: to pay versus maybe using the free version of something

720
00:38:34,840 --> 00:38:35,640
Speaker 10: like a CHATGBT.

721
00:38:36,719 --> 00:38:39,120
Speaker 3: I think what's interesting is for the last couple of

722
00:38:39,160 --> 00:38:42,040
Speaker 3: weeks we've been obsessed about how enterprise is adopting or

723
00:38:42,040 --> 00:38:44,440
Speaker 3: not adopting to efficiency. The MIT report the fact that

724
00:38:44,520 --> 00:38:48,520
Speaker 3: ninety five percent of pilots are ultimately not bringing ROAI.

725
00:38:49,200 --> 00:38:51,719
Speaker 3: When you're thinking about consumer perspective here that some of

726
00:38:51,719 --> 00:38:52,920
Speaker 3: them names.

727
00:38:52,640 --> 00:38:54,120
Speaker 4: Are sort of enterprise and nature.

728
00:38:54,600 --> 00:38:57,520
Speaker 3: Are people getting return on their AI investment to the

729
00:38:57,520 --> 00:38:58,400
Speaker 3: amount that they need to?

730
00:38:58,600 --> 00:39:01,160
Speaker 10: Yeah, we're seeing a big kind of what I call

731
00:39:01,200 --> 00:39:04,040
Speaker 10: the great expansion of consumer software, which is really new

732
00:39:04,080 --> 00:39:06,520
Speaker 10: in the AI era, which is that previously you would

733
00:39:06,560 --> 00:39:09,920
Speaker 10: see consumer companies take years, if not decades to actually

734
00:39:10,200 --> 00:39:13,320
Speaker 10: transition their software to enterprise. Think about Canva took them

735
00:39:13,520 --> 00:39:16,000
Speaker 10: eight plus years to even have a teams plan. Now

736
00:39:16,040 --> 00:39:18,160
Speaker 10: we have companies like eleven Labs that are going from

737
00:39:18,239 --> 00:39:21,440
Speaker 10: zero to hundreds of millions of revenue in two years

738
00:39:21,520 --> 00:39:24,439
Speaker 10: or less. And many of these companies are actually making much,

739
00:39:24,480 --> 00:39:26,920
Speaker 10: if not most, of their revenue from enterprises, which is

740
00:39:26,960 --> 00:39:29,760
Speaker 10: really exciting. We do take a look at the retention data,

741
00:39:29,840 --> 00:39:33,120
Speaker 10: and our data shows that for generative AI products, there's

742
00:39:33,160 --> 00:39:35,320
Speaker 10: a lot of tourism. So if you're a free user,

743
00:39:35,680 --> 00:39:37,719
Speaker 10: you're probably less likely to retain than you would be

744
00:39:37,760 --> 00:39:41,480
Speaker 10: on a comm or a Duolingo or PREAI subscription. But

745
00:39:41,480 --> 00:39:43,759
Speaker 10: if you're a paid user, you're just as likely as

746
00:39:44,000 --> 00:39:46,719
Speaker 10: to retain, So that suggests that the ROI is there,

747
00:39:46,719 --> 00:39:48,960
Speaker 10: at least for those consumer and prosumer users.

748
00:39:49,640 --> 00:39:52,040
Speaker 2: Olivia, we've told you what we're up to. Which apps

749
00:39:52,080 --> 00:39:54,560
Speaker 2: are you using and how are you using them?

750
00:39:54,880 --> 00:39:56,640
Speaker 10: I have a lot of appleing use. I publish my

751
00:39:56,719 --> 00:39:59,600
Speaker 10: whole stack recently. I am a power user of Chat GBT.

752
00:40:00,120 --> 00:40:02,840
Speaker 10: I'm also a power user of Google on their Ultra subscription.

753
00:40:02,920 --> 00:40:05,040
Speaker 10: I'm a big fan of vo three and the new

754
00:40:05,200 --> 00:40:10,360
Speaker 10: Nano Banana Photoshop esque video editing image editing model that

755
00:40:10,400 --> 00:40:12,600
Speaker 10: they launched. I also use a lot of products like

756
00:40:12,640 --> 00:40:15,480
Speaker 10: Krea for creative tools. Gamma used to create almost all

757
00:40:15,480 --> 00:40:18,680
Speaker 10: of my presentations. There's everything as specific as a product

758
00:40:18,719 --> 00:40:21,200
Speaker 10: now called Happenstance that lets you search through your network

759
00:40:21,280 --> 00:40:24,439
Speaker 10: much more easily than LinkedIn. I would say my top

760
00:40:24,480 --> 00:40:26,759
Speaker 10: advice for anyone interested in AI is just to try

761
00:40:26,800 --> 00:40:29,719
Speaker 10: the products because it's absolutely the easiest way to learn.

762
00:40:29,800 --> 00:40:33,400
Speaker 3: When you're thinking about investing. I look at you know

763
00:40:33,440 --> 00:40:35,480
Speaker 3: some of the names on here. Mid Journey is a

764
00:40:35,520 --> 00:40:38,239
Speaker 3: fascinating case study which has never taken money and been

765
00:40:38,280 --> 00:40:42,480
Speaker 3: able to scale and hold on and retain users. Where

766
00:40:42,520 --> 00:40:45,799
Speaker 3: do you think the best dollar is allocated from an

767
00:40:45,800 --> 00:40:46,680
Speaker 3: A sixteen zier the.

768
00:40:46,640 --> 00:40:49,840
Speaker 10: Moment, Yeah, it's a good question. We invest both in

769
00:40:49,880 --> 00:40:54,840
Speaker 10: the model companies themselves, many of which have grown extremely quickly,

770
00:40:54,960 --> 00:40:57,120
Speaker 10: and then at the application layer companies. I think we're

771
00:40:57,200 --> 00:40:59,680
Speaker 10: very focused to your earlier point about companies that are

772
00:41:00,200 --> 00:41:03,480
Speaker 10: vertically oriented and our building for specific users. We talk

773
00:41:03,520 --> 00:41:05,560
Speaker 10: about founders that have kind of an earned secret or

774
00:41:05,600 --> 00:41:09,080
Speaker 10: a special insight and are almost maniacal about building.

775
00:41:08,760 --> 00:41:09,800
Speaker 4: For a specific user.

776
00:41:09,840 --> 00:41:12,719
Speaker 10: And so often these are not the extremely general, broad

777
00:41:12,760 --> 00:41:14,759
Speaker 10: based products that you can do anything on, but are

778
00:41:14,840 --> 00:41:17,200
Speaker 10: products that are really really specific for one or two

779
00:41:17,200 --> 00:41:19,600
Speaker 10: things that users might want to do and almost become

780
00:41:19,640 --> 00:41:21,920
Speaker 10: their system of record or a new core workspace or

781
00:41:21,920 --> 00:41:23,359
Speaker 10: canvas for them to get things done.

782
00:41:24,560 --> 00:41:26,920
Speaker 2: Olivia more Partner and jreson Horror. It's great to have

783
00:41:26,960 --> 00:41:29,319
Speaker 2: you on Boomberg Tech. Thank you very much. Now coming up,

784
00:41:30,040 --> 00:41:34,600
Speaker 2: these beautiful images are from the Hubble telescope, but plans

785
00:41:34,600 --> 00:41:37,160
Speaker 2: for telescopes that could give us an even deeper view

786
00:41:37,200 --> 00:41:40,640
Speaker 2: of space could be delayed following US budget cuts. We're

787
00:41:40,640 --> 00:41:42,520
Speaker 2: going to talk about that next stay with us. This

788
00:41:42,600 --> 00:41:43,640
Speaker 2: is Bloomberg Tech.

789
00:41:55,040 --> 00:41:57,920
Speaker 3: Cuts by the Trump administration are threatening to delay construction

790
00:41:58,040 --> 00:42:01,239
Speaker 3: of a new powerful telescope, one that scientists say can

791
00:42:01,320 --> 00:42:05,239
Speaker 3: fastly expand our understanding of the universe. US astronomers warn

792
00:42:05,280 --> 00:42:08,240
Speaker 3: the setback may give China and edge in space research

793
00:42:08,480 --> 00:42:11,000
Speaker 3: as bring in bluemogs. Bruce Einhorn, who's been covering this story,

794
00:42:11,040 --> 00:42:13,719
Speaker 3: It's a beautifully written sort of deep dive into what

795
00:42:13,760 --> 00:42:15,200
Speaker 3: these funding cuts could mean.

796
00:42:15,520 --> 00:42:18,120
Speaker 4: But why US versus China? Why is this a worry

797
00:42:18,160 --> 00:42:19,840
Speaker 4: from a national security perspective?

798
00:42:19,840 --> 00:42:24,000
Speaker 14: Bruce, Well, the US has long been a leader in astronomy,

799
00:42:24,480 --> 00:42:27,719
Speaker 14: dating back to the end of World War Two, and

800
00:42:27,760 --> 00:42:33,120
Speaker 14: it's part of a broader pushed by the United States

801
00:42:33,160 --> 00:42:36,439
Speaker 14: to invest in science. And you know, there are lots

802
00:42:36,440 --> 00:42:40,560
Speaker 14: of spillover effects from having those sorts of investments. China

803
00:42:40,719 --> 00:42:44,560
Speaker 14: is making big investments in science now. There is concern

804
00:42:45,120 --> 00:42:50,840
Speaker 14: among many scientists that Trump administration's plans to reduce spendings,

805
00:42:50,840 --> 00:42:53,560
Speaker 14: say by the National Science Foundation, will have in effect

806
00:42:53,920 --> 00:42:57,160
Speaker 14: not just on the projects themselves, but also on the

807
00:42:57,200 --> 00:43:01,640
Speaker 14: scientific ecosystem of post and graduate students and all the

808
00:43:01,680 --> 00:43:04,880
Speaker 14: people who are there to make sense of all the

809
00:43:04,960 --> 00:43:05,800
Speaker 14: data that gets.

810
00:43:05,600 --> 00:43:09,440
Speaker 2: Generated Bruce, just really quick, is there any evidence that

811
00:43:09,480 --> 00:43:11,880
Speaker 2: the US government kind of heeds the warning from the

812
00:43:11,920 --> 00:43:13,719
Speaker 2: scientific community here in America.

813
00:43:14,920 --> 00:43:18,239
Speaker 14: Well, we're still waiting to find out just what the

814
00:43:18,560 --> 00:43:21,800
Speaker 14: budget allocation will be. Of course, the budget process is

815
00:43:21,840 --> 00:43:25,400
Speaker 14: still very much underway. The administration had proposed a pretty

816
00:43:25,440 --> 00:43:30,600
Speaker 14: big cut, more than fifty percent to the National Science

817
00:43:30,680 --> 00:43:34,399
Speaker 14: Foundation as part of its budget proposal. There has been

818
00:43:34,400 --> 00:43:39,080
Speaker 14: some pushback from Capitol Hill about that, and we're still

819
00:43:39,080 --> 00:43:41,920
Speaker 14: waiting to see just how much money will get allocated.

820
00:43:42,880 --> 00:43:45,799
Speaker 2: Bloomberg's Bruce Einhorn, thank you very much. That does it

821
00:43:45,840 --> 00:43:49,920
Speaker 2: for this edition of Bloomberg Tech. Carrow markets in focus

822
00:43:49,960 --> 00:43:52,720
Speaker 2: because of the mag seven declines. But what an episode

823
00:43:52,719 --> 00:43:54,320
Speaker 2: it has been to start a short week.

824
00:43:54,320 --> 00:43:56,799
Speaker 3: Only twelve stocks eleven now in the green run then

825
00:43:56,840 --> 00:43:57,680
Speaker 3: as that one hundred.

826
00:43:57,719 --> 00:43:59,360
Speaker 4: Don't forget to check out all.

827
00:43:59,280 --> 00:44:01,000
Speaker 3: Of our coverage on our podcast to find on the

828
00:44:01,080 --> 00:44:03,120
Speaker 3: terminal as well as online on Apple Spotify.

829
00:44:03,200 --> 00:44:05,239
Speaker 4: And iHeart great to have you back in. This is

830
00:44:05,280 --> 00:44:05,960
Speaker 4: Boomberg Tech